This paper propose to combine pretrained language models with the modular dialogue paradigm for open-domain dialogue modeling. Our method, semantic-enhanced finetuning, instantiates conversation understanding, planning, and response generation as a language model finetuning task. At inference, we disentangle semantic and token variations by specifying sampling methods and constraints for each module separately. For training and evaluation, we present X-Weibo, a Chinese multi-turn open-domain dialogue dataset with automatic annotation for emotions, DAs, and topical words. Experiments show that semantic-enhanced finetuning outperforms strong baselines on non-semantic and semantic metrics, improves the human-evaluated relevance, coherence, and informativeness, and exhibits considerable controllability over semantic variables.
@article{arxiv.2106.03065,
title = {Semantic-Enhanced Explainable Finetuning for Open-Domain Dialogues},
author = {Yinhe Zheng and Yida Wang and Pei Ke and Zhenyu Yang and Minlie Huang},
journal= {arXiv preprint arXiv:2106.03065},
year = {2022}
}